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Genomic diagnosis for children with intellectual disability and/or developmental delay

Overview of attention for article published in Genome Medicine, May 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
1 blog
policy
1 policy source
twitter
26 X users
facebook
2 Facebook pages

Citations

dimensions_citation
194 Dimensions

Readers on

mendeley
228 Mendeley
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Title
Genomic diagnosis for children with intellectual disability and/or developmental delay
Published in
Genome Medicine, May 2017
DOI 10.1186/s13073-017-0433-1
Pubmed ID
Authors

Kevin M. Bowling, Michelle L. Thompson, Michelle D. Amaral, Candice R. Finnila, Susan M. Hiatt, Krysta L. Engel, J. Nicholas Cochran, Kyle B. Brothers, Kelly M. East, David E. Gray, Whitley V. Kelley, Neil E. Lamb, Edward J. Lose, Carla A. Rich, Shirley Simmons, Jana S. Whittle, Benjamin T. Weaver, Amy S. Nesmith, Richard M. Myers, Gregory S. Barsh, E. Martina Bebin, Gregory M. Cooper

Abstract

Developmental disabilities have diverse genetic causes that must be identified to facilitate precise diagnoses. We describe genomic data from 371 affected individuals, 309 of which were sequenced as proband-parent trios. Whole-exome sequences (WES) were generated for 365 individuals (127 affected) and whole-genome sequences (WGS) were generated for 612 individuals (244 affected). Pathogenic or likely pathogenic variants were found in 100 individuals (27%), with variants of uncertain significance in an additional 42 (11.3%). We found that a family history of neurological disease, especially the presence of an affected first-degree relative, reduces the pathogenic/likely pathogenic variant identification rate, reflecting both the disease relevance and ease of interpretation of de novo variants. We also found that improvements to genetic knowledge facilitated interpretation changes in many cases. Through systematic reanalyses, we have thus far reclassified 15 variants, with 11.3% of families who initially were found to harbor a VUS and 4.7% of families with a negative result eventually found to harbor a pathogenic or likely pathogenic variant. To further such progress, the data described here are being shared through ClinVar, GeneMatcher, and dbGaP. Our data strongly support the value of large-scale sequencing, especially WGS within proband-parent trios, as both an effective first-choice diagnostic tool and means to advance clinical and research progress related to pediatric neurological disease.

X Demographics

X Demographics

The data shown below were collected from the profiles of 26 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 228 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 228 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 34 15%
Researcher 31 14%
Student > Ph. D. Student 25 11%
Student > Doctoral Student 21 9%
Student > Bachelor 19 8%
Other 29 13%
Unknown 69 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 66 29%
Medicine and Dentistry 29 13%
Agricultural and Biological Sciences 20 9%
Neuroscience 14 6%
Psychology 7 3%
Other 16 7%
Unknown 76 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 29. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 April 2019.
All research outputs
#1,310,873
of 24,820,264 outputs
Outputs from Genome Medicine
#276
of 1,529 outputs
Outputs of similar age
#26,034
of 321,303 outputs
Outputs of similar age from Genome Medicine
#7
of 30 outputs
Altmetric has tracked 24,820,264 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,529 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.1. This one has done well, scoring higher than 82% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 321,303 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.